Uses of Package
de.jstacs.sequenceScores.statisticalModels.differentiable

Packages that use de.jstacs.sequenceScores.statisticalModels.differentiable

Package	Description
de.jstacs.classifiers	This package provides the framework for any classifier.
de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix	Provides an implementation of a classifier that allows to train the parameters of a set of DifferentiableStatisticalModels by a unified generative-discriminative learning principle.
de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior	Provides a general definition of a parameter log-prior and a number of implementations of Laplace and Gaussian priors.
de.jstacs.classifiers.differentiableSequenceScoreBased.sampling	Provides the classes for AbstractScoreBasedClassifiers that are based on SamplingDifferentiableStatisticalModels and that sample parameters using the Metropolis-Hastings algorithm.
de.jstacs.sequenceScores.statisticalModels.differentiable	Provides all DifferentiableStatisticalModels, which can compute the gradient with respect to their parameters for a given input Sequence.
de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels	Provides DifferentiableStatisticalModels that are directed graphical models.
de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous	Provides DifferentiableStatisticalModels that are homogeneous, i.e.
de.jstacs.sequenceScores.statisticalModels.differentiable.localMixture
de.jstacs.sequenceScores.statisticalModels.differentiable.mixture	Provides DifferentiableSequenceScores that are mixtures of other DifferentiableSequenceScores.
de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif
de.jstacs.sequenceScores.statisticalModels.trainable	Provides all TrainableStatisticalModels, which can be learned from a single DataSet.
de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models	The package provides different implementations of hidden Markov models based on AbstractHMM.

Classes in de.jstacs.sequenceScores.statisticalModels.differentiable used by de.jstacs.classifiers

Class and Description
DifferentiableStatisticalModel The interface for normalizable DifferentiableSequenceScores.

Classes in de.jstacs.sequenceScores.statisticalModels.differentiable used by de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix

Class and Description
DifferentiableStatisticalModel The interface for normalizable DifferentiableSequenceScores.

Classes in de.jstacs.sequenceScores.statisticalModels.differentiable used by de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior

Class and Description
DifferentiableStatisticalModel The interface for normalizable DifferentiableSequenceScores.

Classes in de.jstacs.sequenceScores.statisticalModels.differentiable used by de.jstacs.classifiers.differentiableSequenceScoreBased.sampling

Class and Description
SamplingDifferentiableStatisticalModel Interface for DifferentiableStatisticalModels that can be used for Metropolis-Hastings sampling in a SamplingScoreBasedClassifier.

Classes in de.jstacs.sequenceScores.statisticalModels.differentiable used by de.jstacs.sequenceScores.statisticalModels.differentiable

Class and Description
AbstractDifferentiableStatisticalModel This class is the main part of any ScoreClassifier.
AbstractVariableLengthDiffSM This abstract class implements some methods declared in DifferentiableStatisticalModel based on the declaration of methods in VariableLengthDiffSM.
CyclicMarkovModelDiffSM This scoring function implements a cyclic Markov model of arbitrary order and periodicity for any sequence length.
DifferentiableStatisticalModel The interface for normalizable DifferentiableSequenceScores.
IndependentProductDiffSM This class enables the user to model parts of a sequence independent of each other.
MappingDiffSM This class implements a DifferentiableStatisticalModel that works on mapped Sequences.
MarkovRandomFieldDiffSM This class implements the scoring function for any MRF (Markov Random Field).
NormalizedDiffSM This class makes an unnormalized DifferentiableStatisticalModel to a normalized DifferentiableStatisticalModel.
SamplingDifferentiableStatisticalModel Interface for DifferentiableStatisticalModels that can be used for Metropolis-Hastings sampling in a SamplingScoreBasedClassifier.
VariableLengthDiffSM This is an interface for all DifferentiableStatisticalModels that allow to score subsequences of arbitrary length.

Classes in de.jstacs.sequenceScores.statisticalModels.differentiable used by de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels

Class and Description
AbstractDifferentiableStatisticalModel This class is the main part of any ScoreClassifier.
DifferentiableStatisticalModel The interface for normalizable DifferentiableSequenceScores.
SamplingDifferentiableStatisticalModel Interface for DifferentiableStatisticalModels that can be used for Metropolis-Hastings sampling in a SamplingScoreBasedClassifier.

Classes in de.jstacs.sequenceScores.statisticalModels.differentiable used by de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous

Class and Description
AbstractDifferentiableStatisticalModel This class is the main part of any ScoreClassifier.
AbstractVariableLengthDiffSM This abstract class implements some methods declared in DifferentiableStatisticalModel based on the declaration of methods in VariableLengthDiffSM.
DifferentiableStatisticalModel The interface for normalizable DifferentiableSequenceScores.
SamplingDifferentiableStatisticalModel Interface for DifferentiableStatisticalModels that can be used for Metropolis-Hastings sampling in a SamplingScoreBasedClassifier.
VariableLengthDiffSM This is an interface for all DifferentiableStatisticalModels that allow to score subsequences of arbitrary length.

Classes in de.jstacs.sequenceScores.statisticalModels.differentiable used by de.jstacs.sequenceScores.statisticalModels.differentiable.localMixture

Class and Description
AbstractDifferentiableStatisticalModel This class is the main part of any ScoreClassifier.
DifferentiableStatisticalModel The interface for normalizable DifferentiableSequenceScores.

Classes in de.jstacs.sequenceScores.statisticalModels.differentiable used by de.jstacs.sequenceScores.statisticalModels.differentiable.mixture

Class and Description
AbstractDifferentiableStatisticalModel This class is the main part of any ScoreClassifier.
DifferentiableStatisticalModel The interface for normalizable DifferentiableSequenceScores.
SamplingDifferentiableStatisticalModel Interface for DifferentiableStatisticalModels that can be used for Metropolis-Hastings sampling in a SamplingScoreBasedClassifier.
VariableLengthDiffSM This is an interface for all DifferentiableStatisticalModels that allow to score subsequences of arbitrary length.

Classes in de.jstacs.sequenceScores.statisticalModels.differentiable used by de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif

Class and Description
AbstractDifferentiableStatisticalModel This class is the main part of any ScoreClassifier.
DifferentiableStatisticalModel The interface for normalizable DifferentiableSequenceScores.
SamplingDifferentiableStatisticalModel Interface for DifferentiableStatisticalModels that can be used for Metropolis-Hastings sampling in a SamplingScoreBasedClassifier.

Classes in de.jstacs.sequenceScores.statisticalModels.differentiable used by de.jstacs.sequenceScores.statisticalModels.trainable

Class and Description
DifferentiableStatisticalModel The interface for normalizable DifferentiableSequenceScores.

Classes in de.jstacs.sequenceScores.statisticalModels.differentiable used by de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models

Class and Description
DifferentiableStatisticalModel The interface for normalizable DifferentiableSequenceScores.
SamplingDifferentiableStatisticalModel Interface for DifferentiableStatisticalModels that can be used for Metropolis-Hastings sampling in a SamplingScoreBasedClassifier.